One-Shot Face Stylization

Abstract

The abstract delves into the intricate realm of one-shot face stylization, a captivating domain within computer vision and deep learning. It revolves around the art of transforming a target face using a reference image, navigating the delicate balance between retaining facial recognition and infusing desired stylistic traits. This pursuit has garnered considerable attention owing to its myriad applications across digital art, entertainment, and personalized products. The abstract scrutinizes the essential components of one-shot face stylization, highlighting the pivotal role of deep neural networks, particularly generative adversarial networks (GANs). These networks are adept at crafting bespoke facial images by assimilating information from both the target and reference faces, leveraging the reference image as a guiding beacon. The crux of success in one-shot facial stylization lies in the meticulous orchestration of the fading process, which harmonizes the preservation of identity with the enhancement of artistic technique. As advancements in this field continue to unfold, the potential ramifications span far and wide, promising to revolutionize creative expression and customization across diverse industries, spanning from digital art and animation to virtual avatars and social media filters. Key Words: One-Shot face stylization, Generative adversarial network, Facial recognition, Digital art, Personalization